Improving potato leaf chlorophyll content prediction using a machine learning model with a hybrid dataset
2
Key Laboratory of Agricultural Ecological Security and Green Development at Universities of Inner Mongolia Autonomous
Publication type: Journal Article
Publication date: 2025-02-21
scimago Q2
wos Q3
SJR: 0.676
CiteScore: 5.9
Impact factor: 2.6
ISSN: 01431161, 13665901
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Total citations:
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Citations from 2024:
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(100%)
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GOST
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Yang Haibo et al. Improving potato leaf chlorophyll content prediction using a machine learning model with a hybrid dataset // International Journal of Remote Sensing. 2025. Vol. 46. No. 8. pp. 3064-3088.
GOST all authors (up to 50)
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Yang Haibo, Hu Y., Yin Hang, Jin Q., Li F., Yu K. Improving potato leaf chlorophyll content prediction using a machine learning model with a hybrid dataset // International Journal of Remote Sensing. 2025. Vol. 46. No. 8. pp. 3064-3088.
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TY - JOUR
DO - 10.1080/01431161.2025.2465916
UR - https://www.tandfonline.com/doi/full/10.1080/01431161.2025.2465916
TI - Improving potato leaf chlorophyll content prediction using a machine learning model with a hybrid dataset
T2 - International Journal of Remote Sensing
AU - Yang Haibo
AU - Hu, Yuncai
AU - Yin Hang
AU - Jin, Qingyu
AU - Li, Fei
AU - Yu, Kang
PY - 2025
DA - 2025/02/21
PB - Taylor & Francis
SP - 3064-3088
IS - 8
VL - 46
SN - 0143-1161
SN - 1366-5901
ER -
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BibTex (up to 50 authors)
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@article{2025_Yang Haibo,
author = {Yang Haibo and Yuncai Hu and Yin Hang and Qingyu Jin and Fei Li and Kang Yu},
title = {Improving potato leaf chlorophyll content prediction using a machine learning model with a hybrid dataset},
journal = {International Journal of Remote Sensing},
year = {2025},
volume = {46},
publisher = {Taylor & Francis},
month = {feb},
url = {https://www.tandfonline.com/doi/full/10.1080/01431161.2025.2465916},
number = {8},
pages = {3064--3088},
doi = {10.1080/01431161.2025.2465916}
}
Cite this
MLA
Copy
Yang Haibo, et al. “Improving potato leaf chlorophyll content prediction using a machine learning model with a hybrid dataset.” International Journal of Remote Sensing, vol. 46, no. 8, Feb. 2025, pp. 3064-3088. https://www.tandfonline.com/doi/full/10.1080/01431161.2025.2465916.